Overview

Dataset statistics

Number of variables23
Number of observations1359
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory196.5 KiB
Average record size in memory148.1 B

Variable types

Text2
Numeric13
Categorical8

Alerts

Battery capacity (mAh) is highly overall correlated with Screen size (inches) and 9 other fieldsHigh correlation
Screen size (inches) is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
Resolution x is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
Resolution y is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
Processor is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
RAM (MB) is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
Internal storage (GB) is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
Rear camera is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
Front camera is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
Price is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
logTranforedPrice is highly overall correlated with Battery capacity (mAh) and 9 other fieldsHigh correlation
4G/ LTE is highly overall correlated with latest_tech_stackHigh correlation
latest_tech_stack is highly overall correlated with 4G/ LTEHigh correlation
Touchscreen is highly imbalanced (90.3%)Imbalance
Wi-Fi is highly imbalanced (94.8%)Imbalance
Bluetooth is highly imbalanced (91.2%)Imbalance
GPS is highly imbalanced (60.0%)Imbalance
Number of SIMs is highly imbalanced (58.4%)Imbalance
3G is highly imbalanced (51.0%)Imbalance
Name has unique valuesUnique
Processor has 42 (3.1%) zerosZeros
Front camera has 18 (1.3%) zerosZeros
Operating system has 1299 (95.6%) zerosZeros

Reproduction

Analysis started2024-04-13 13:56:23.309488
Analysis finished2024-04-13 13:56:59.272836
Duration35.96 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Name
Text

UNIQUE 

Distinct1359
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2024-04-13T09:56:59.729905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length34
Median length27
Mean length14.997792
Min length5

Characters and Unicode

Total characters20382
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1359 ?
Unique (%)100.0%

Sample

1st rowOnePlus 7T Pro McLaren Edition
2nd rowRealme X2 Pro
3rd rowiPhone 11 Pro Max
4th rowiPhone 11
5th rowLG G8X ThinQ
ValueCountFrequency (%)
intex 117
 
2.8%
samsung 101
 
2.4%
galaxy 98
 
2.4%
aqua 87
 
2.1%
pro 75
 
1.8%
micromax 71
 
1.7%
2 64
 
1.6%
lava 59
 
1.4%
plus 56
 
1.4%
panasonic 55
 
1.3%
Other values (941) 3340
81.0%
2024-04-13T09:57:00.505335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2764
 
13.6%
o 1458
 
7.2%
a 1414
 
6.9%
e 1126
 
5.5%
n 1020
 
5.0%
i 1010
 
5.0%
l 650
 
3.2%
r 611
 
3.0%
u 576
 
2.8%
t 524
 
2.6%
Other values (58) 9229
45.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11563
56.7%
Uppercase Letter 4118
 
20.2%
Space Separator 2764
 
13.6%
Decimal Number 1752
 
8.6%
Other Punctuation 48
 
0.2%
Open Punctuation 43
 
0.2%
Close Punctuation 43
 
0.2%
Math Symbol 43
 
0.2%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 352
 
8.5%
S 331
 
8.0%
A 326
 
7.9%
G 302
 
7.3%
M 295
 
7.2%
L 293
 
7.1%
I 238
 
5.8%
C 212
 
5.1%
X 181
 
4.4%
Z 166
 
4.0%
Other values (16) 1422
34.5%
Lowercase Letter
ValueCountFrequency (%)
o 1458
12.6%
a 1414
12.2%
e 1126
9.7%
n 1020
 
8.8%
i 1010
 
8.7%
l 650
 
5.6%
r 611
 
5.3%
u 576
 
5.0%
t 524
 
4.5%
s 510
 
4.4%
Other values (15) 2664
23.0%
Decimal Number
ValueCountFrequency (%)
1 293
16.7%
0 251
14.3%
2 236
13.5%
5 236
13.5%
3 171
9.8%
4 153
8.7%
7 127
7.2%
6 118
6.7%
8 95
 
5.4%
9 72
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 47
97.9%
' 1
 
2.1%
Space Separator
ValueCountFrequency (%)
2764
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Math Symbol
ValueCountFrequency (%)
+ 43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15681
76.9%
Common 4701
 
23.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1458
 
9.3%
a 1414
 
9.0%
e 1126
 
7.2%
n 1020
 
6.5%
i 1010
 
6.4%
l 650
 
4.1%
r 611
 
3.9%
u 576
 
3.7%
t 524
 
3.3%
s 510
 
3.3%
Other values (41) 6782
43.2%
Common
ValueCountFrequency (%)
2764
58.8%
1 293
 
6.2%
0 251
 
5.3%
2 236
 
5.0%
5 236
 
5.0%
3 171
 
3.6%
4 153
 
3.3%
7 127
 
2.7%
6 118
 
2.5%
8 95
 
2.0%
Other values (7) 257
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2764
 
13.6%
o 1458
 
7.2%
a 1414
 
6.9%
e 1126
 
5.5%
n 1020
 
5.0%
i 1010
 
5.0%
l 650
 
3.2%
r 611
 
3.0%
u 576
 
2.8%
t 524
 
2.6%
Other values (58) 9229
45.3%

Brand
Text

Distinct76
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2024-04-13T09:57:00.861125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.5283297
Min length2

Characters and Unicode

Total characters7513
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.0%

Sample

1st rowOnePlus
2nd rowRealme
3rd rowApple
4th rowApple
5th rowLG
ValueCountFrequency (%)
intex 117
 
8.6%
samsung 101
 
7.4%
micromax 71
 
5.2%
lava 59
 
4.3%
panasonic 55
 
4.0%
vivo 52
 
3.8%
xiaomi 47
 
3.4%
lenovo 42
 
3.1%
motorola 42
 
3.1%
lg 42
 
3.1%
Other values (68) 735
53.9%
2024-04-13T09:57:01.352805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 968
 
12.9%
a 680
 
9.1%
n 652
 
8.7%
i 537
 
7.1%
e 452
 
6.0%
m 276
 
3.7%
s 270
 
3.6%
l 241
 
3.2%
r 236
 
3.1%
u 231
 
3.1%
Other values (42) 2970
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5951
79.2%
Uppercase Letter 1542
 
20.5%
Decimal Number 10
 
0.1%
Other Punctuation 5
 
0.1%
Space Separator 4
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 968
16.3%
a 680
11.4%
n 652
11.0%
i 537
 
9.0%
e 452
 
7.6%
m 276
 
4.6%
s 270
 
4.5%
l 241
 
4.0%
r 236
 
4.0%
u 231
 
3.9%
Other values (15) 1408
23.7%
Uppercase Letter
ValueCountFrequency (%)
L 185
12.0%
I 168
 
10.9%
S 156
 
10.1%
M 133
 
8.6%
G 85
 
5.5%
H 84
 
5.4%
V 81
 
5.3%
P 74
 
4.8%
X 72
 
4.7%
C 71
 
4.6%
Other values (12) 433
28.1%
Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7493
99.7%
Common 20
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 968
 
12.9%
a 680
 
9.1%
n 652
 
8.7%
i 537
 
7.2%
e 452
 
6.0%
m 276
 
3.7%
s 270
 
3.6%
l 241
 
3.2%
r 236
 
3.1%
u 231
 
3.1%
Other values (37) 2950
39.4%
Common
ValueCountFrequency (%)
1 5
25.0%
0 5
25.0%
. 5
25.0%
4
20.0%
- 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 968
 
12.9%
a 680
 
9.1%
n 652
 
8.7%
i 537
 
7.1%
e 452
 
6.0%
m 276
 
3.7%
s 270
 
3.6%
l 241
 
3.2%
r 236
 
3.1%
u 231
 
3.1%
Other values (42) 2970
39.5%

Model
Real number (ℝ)

Distinct1321
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean654.35762
Minimum0
Maximum1320
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-13T09:57:01.587824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile52.9
Q1319.5
median656
Q3988.5
95-th percentile1252.1
Maximum1320
Range1320
Interquartile range (IQR)669

Descriptive statistics

Standard deviation385.27291
Coefficient of variation (CV)0.58878036
Kurtosis-1.208393
Mean654.35762
Median Absolute Deviation (MAD)334
Skewness-0.0034659963
Sum889272
Variance148435.22
MonotonicityNot monotonic
2024-04-13T09:57:01.819926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1071 4
 
0.3%
22 3
 
0.2%
1212 3
 
0.2%
10 3
 
0.2%
6 3
 
0.2%
32 3
 
0.2%
51 3
 
0.2%
0 3
 
0.2%
481 3
 
0.2%
1220 2
 
0.1%
Other values (1311) 1329
97.8%
ValueCountFrequency (%)
0 3
0.2%
1 2
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 3
0.2%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
1320 1
0.1%
1319 1
0.1%
1318 1
0.1%
1317 1
0.1%
1316 1
0.1%
1315 1
0.1%
1314 1
0.1%
1313 1
0.1%
1312 1
0.1%
1311 1
0.1%

Battery capacity (mAh)
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4485615
Minimum3.0043214
Maximum3.7781513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-13T09:57:02.033881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.0043214
5-th percentile3.2304489
Q13.3617278
median3.4771213
Q33.544068
95-th percentile3.6532125
Maximum3.7781513
Range0.77382988
Interquartile range (IQR)0.18234021

Descriptive statistics

Standard deviation0.13221491
Coefficient of variation (CV)0.038339149
Kurtosis-0.20100802
Mean3.4485615
Median Absolute Deviation (MAD)0.096910013
Skewness-0.25488958
Sum4686.5951
Variance0.017480784
MonotonicityNot monotonic
2024-04-13T09:57:02.241505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.477121255 182
 
13.4%
3.602059991 145
 
10.7%
3.301029996 115
 
8.5%
3.397940009 91
 
6.7%
3.698970004 49
 
3.6%
3.361727836 49
 
3.6%
3.414973348 41
 
3.0%
3.51851394 34
 
2.5%
3.447158031 34
 
2.5%
3.544068044 32
 
2.4%
Other values (155) 587
43.2%
ValueCountFrequency (%)
3.004321374 1
 
0.1%
3.021189299 1
 
0.1%
3.079181246 3
 
0.2%
3.096910013 2
 
0.1%
3.113943352 6
 
0.4%
3.120573931 1
 
0.1%
3.130333768 1
 
0.1%
3.146128036 16
1.2%
3.155336037 2
 
0.1%
3.161368002 7
0.5%
ValueCountFrequency (%)
3.77815125 3
 
0.2%
3.72427587 1
 
0.1%
3.707570176 1
 
0.1%
3.700703717 1
 
0.1%
3.698970004 49
3.6%
3.69019608 1
 
0.1%
3.685741739 3
 
0.2%
3.658011397 1
 
0.1%
3.653212514 14
 
1.0%
3.641474111 1
 
0.1%

Screen size (inches)
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71983064
Minimum0.38021124
Maximum0.86332286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-13T09:57:02.474410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.38021124
5-th percentile0.60205999
Q10.69897
median0.71600334
Q30.75587486
95-th percentile0.80617997
Maximum0.86332286
Range0.48311162
Interquartile range (IQR)0.056904851

Descriptive statistics

Standard deviation0.058208421
Coefficient of variation (CV)0.08086405
Kurtosis3.7486273
Mean0.71983064
Median Absolute Deviation (MAD)0.024359346
Skewness-1.0639336
Sum978.24984
Variance0.0033882202
MonotonicityNot monotonic
2024-04-13T09:57:02.674853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6989700043 404
29.7%
0.7403626895 251
18.5%
0.7160033436 82
 
6.0%
0.6532125138 80
 
5.9%
0.6020599913 71
 
5.2%
0.7781512504 53
 
3.9%
0.7558748557 51
 
3.8%
0.6720978579 37
 
2.7%
0.7993405495 28
 
2.1%
0.7363965023 25
 
1.8%
Other values (70) 277
20.4%
ValueCountFrequency (%)
0.3802112417 1
 
0.1%
0.3873898263 1
 
0.1%
0.3891660844 1
 
0.1%
0.414973348 1
 
0.1%
0.4471580313 2
0.1%
0.4771212547 2
0.1%
0.4913616938 2
0.1%
0.5051499783 1
 
0.1%
0.5440680444 4
0.3%
0.5682017241 1
 
0.1%
ValueCountFrequency (%)
0.8633228601 1
 
0.1%
0.84509804 1
 
0.1%
0.8388490907 1
 
0.1%
0.8325089127 1
 
0.1%
0.8260748027 7
0.5%
0.8241258339 4
0.3%
0.8228216453 2
 
0.1%
0.8195439355 2
 
0.1%
0.8188854146 4
0.3%
0.8162413 2
 
0.1%

Touchscreen
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
1
1342 
0
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1359
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1342
98.7%
0 17
 
1.3%

Length

2024-04-13T09:57:02.852433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T09:57:03.002268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1342
98.7%
0 17
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 1342
98.7%
0 17
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1359
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1342
98.7%
0 17
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1342
98.7%
0 17
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1342
98.7%
0 17
 
1.3%

Resolution x
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8854323
Minimum2.3802112
Maximum3.3344538
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-13T09:57:03.129868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.3802112
5-th percentile2.6812412
Q12.8573325
median2.8573325
Q33.0334238
95-th percentile3.1583625
Maximum3.3344538
Range0.95424251
Interquartile range (IQR)0.17609126

Descriptive statistics

Standard deviation0.14529526
Coefficient of variation (CV)0.050354763
Kurtosis-0.054220768
Mean2.8854323
Median Absolute Deviation (MAD)0.17609126
Skewness-0.14064136
Sum3921.3025
Variance0.021110713
MonotonicityNot monotonic
2024-04-13T09:57:03.300951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2.857332496 585
43.0%
3.033423755 373
27.4%
2.681241237 244
18.0%
3.158362492 65
 
4.8%
2.73239376 33
 
2.4%
2.380211242 9
 
0.7%
3.10720997 6
 
0.4%
2.931457871 6
 
0.4%
2.806179974 5
 
0.4%
2.88536122 4
 
0.3%
Other values (18) 29
 
2.1%
ValueCountFrequency (%)
2.380211242 9
 
0.7%
2.505149978 1
 
0.1%
2.556302501 2
 
0.1%
2.602059991 2
 
0.1%
2.681241237 244
18.0%
2.73239376 33
 
2.4%
2.748188027 1
 
0.1%
2.77815125 1
 
0.1%
2.806179974 5
 
0.4%
2.857332496 585
43.0%
ValueCountFrequency (%)
3.334453751 2
 
0.1%
3.274157849 1
 
0.1%
3.204119983 1
 
0.1%
3.186391216 1
 
0.1%
3.181843588 1
 
0.1%
3.158362492 65
4.8%
3.10720997 6
 
0.4%
3.094121596 2
 
0.1%
3.070407322 1
 
0.1%
3.051152522 3
 
0.2%

Resolution y
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1423458
Minimum2.50515
Maximum3.5843312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-13T09:57:03.505067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.50515
5-th percentile2.90309
Q13.10721
median3.10721
Q33.2833012
95-th percentile3.40824
Maximum3.5843312
Range1.0791812
Interquartile range (IQR)0.17609126

Descriptive statistics

Standard deviation0.16793735
Coefficient of variation (CV)0.053443306
Kurtosis0.36508386
Mean3.1423458
Median Absolute Deviation (MAD)0.1757521
Skewness-0.35321479
Sum4270.448
Variance0.028202954
MonotonicityNot monotonic
2024-04-13T09:57:03.724383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.10720997 439
32.3%
3.283301229 226
16.6%
2.931457871 152
 
11.2%
2.903089987 84
 
6.2%
3.158362492 68
 
5.0%
3.369215857 58
 
4.3%
2.982271233 43
 
3.2%
3.408239965 42
 
3.1%
3.181843588 40
 
2.9%
3.334453751 36
 
2.6%
Other values (43) 171
 
12.6%
ValueCountFrequency (%)
2.505149978 8
 
0.6%
2.681241237 11
 
0.8%
2.685741739 1
 
0.1%
2.766412847 1
 
0.1%
2.77815125 1
 
0.1%
2.806179974 1
 
0.1%
2.857332496 10
 
0.7%
2.903089987 84
6.2%
2.931457871 152
11.2%
2.982271233 43
 
3.2%
ValueCountFrequency (%)
3.584331224 2
 
0.1%
3.505149978 3
 
0.2%
3.494154594 7
 
0.5%
3.482873584 2
 
0.1%
3.471291711 7
 
0.5%
3.459392488 4
 
0.3%
3.429429264 2
 
0.1%
3.420945406 1
 
0.1%
3.408239965 42
3.1%
3.401400541 2
 
0.1%

Processor
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70240044
Minimum0
Maximum1
Zeros42
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-13T09:57:04.364573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30103
Q10.60205999
median0.60205999
Q30.90308999
95-th percentile0.90308999
Maximum1
Range1
Interquartile range (IQR)0.30103

Descriptive statistics

Standard deviation0.20679283
Coefficient of variation (CV)0.29440874
Kurtosis2.0122855
Mean0.70240044
Median Absolute Deviation (MAD)0
Skewness-1.1235955
Sum954.56219
Variance0.042763273
MonotonicityNot monotonic
2024-04-13T09:57:04.521704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.6020599913 683
50.3%
0.903089987 565
41.6%
0.3010299957 45
 
3.3%
0 42
 
3.1%
0.7781512504 20
 
1.5%
1 4
 
0.3%
ValueCountFrequency (%)
0 42
 
3.1%
0.3010299957 45
 
3.3%
0.6020599913 683
50.3%
0.7781512504 20
 
1.5%
0.903089987 565
41.6%
1 4
 
0.3%
ValueCountFrequency (%)
1 4
 
0.3%
0.903089987 565
41.6%
0.7781512504 20
 
1.5%
0.6020599913 683
50.3%
0.3010299957 45
 
3.3%
0 42
 
3.1%

RAM (MB)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3003708
Minimum1.80618
Maximum4.0791812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-13T09:57:04.675329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.80618
5-th percentile2.70927
Q13
median3.30103
Q33.4771213
95-th percentile3.7781513
Maximum4.0791812
Range2.2730013
Interquartile range (IQR)0.47712125

Descriptive statistics

Standard deviation0.30187837
Coefficient of variation (CV)0.091468015
Kurtosis0.38057907
Mean3.3003708
Median Absolute Deviation (MAD)0.30103
Skewness-0.42170903
Sum4485.204
Variance0.09113055
MonotonicityNot monotonic
2024-04-13T09:57:04.844591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 353
26.0%
3.301029996 342
25.2%
3.477121255 282
20.8%
3.602059991 200
14.7%
2.709269961 72
 
5.3%
3.77815125 63
 
4.6%
3.903089987 29
 
2.1%
2.408239965 8
 
0.6%
4.079181246 4
 
0.3%
1.806179974 2
 
0.1%
Other values (3) 4
 
0.3%
ValueCountFrequency (%)
1.806179974 2
 
0.1%
2.408239965 8
 
0.6%
2.460897843 1
 
0.1%
2.584331224 1
 
0.1%
2.709269961 72
 
5.3%
2.88536122 2
 
0.1%
3 353
26.0%
3.301029996 342
25.2%
3.477121255 282
20.8%
3.602059991 200
14.7%
ValueCountFrequency (%)
4.079181246 4
 
0.3%
3.903089987 29
 
2.1%
3.77815125 63
 
4.6%
3.602059991 200
14.7%
3.477121255 282
20.8%
3.301029996 342
25.2%
3 353
26.0%
2.88536122 2
 
0.1%
2.709269961 72
 
5.3%
2.584331224 1
 
0.1%

Internal storage (GB)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2845979
Minimum-1.19382
Maximum2.70927
Zeros2
Zeros (%)0.1%
Negative12
Negative (%)0.9%
Memory size10.7 KiB
2024-04-13T09:57:05.004629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.19382
5-th percentile0.60205999
Q10.90308999
median1.20412
Q31.50515
95-th percentile2.10721
Maximum2.70927
Range3.90309
Interquartile range (IQR)0.60205999

Descriptive statistics

Standard deviation0.42310681
Coefficient of variation (CV)0.32936907
Kurtosis2.1944528
Mean1.2845979
Median Absolute Deviation (MAD)0.30103
Skewness-0.30024583
Sum1745.7685
Variance0.17901937
MonotonicityNot monotonic
2024-04-13T09:57:05.146711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1.204119983 419
30.8%
0.903089987 321
23.6%
1.505149978 284
20.9%
1.806179974 178
13.1%
2.10720997 69
 
5.1%
0.6020599913 62
 
4.6%
2.408239965 9
 
0.7%
-0.290730039 9
 
0.7%
0 2
 
0.1%
-1.193820026 1
 
0.1%
Other values (5) 5
 
0.4%
ValueCountFrequency (%)
-1.193820026 1
 
0.1%
-0.8927900304 1
 
0.1%
-0.7958800173 1
 
0.1%
-0.290730039 9
 
0.7%
0 2
 
0.1%
0.3010299957 1
 
0.1%
0.4771212547 1
 
0.1%
0.6020599913 62
 
4.6%
0.903089987 321
23.6%
1.204119983 419
30.8%
ValueCountFrequency (%)
2.709269961 1
 
0.1%
2.408239965 9
 
0.7%
2.10720997 69
 
5.1%
1.806179974 178
13.1%
1.505149978 284
20.9%
1.204119983 419
30.8%
0.903089987 321
23.6%
0.6020599913 62
 
4.6%
0.4771212547 1
 
0.1%
0.3010299957 1
 
0.1%

Rear camera
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0010903
Minimum-0.52287875
Maximum2.0334238
Zeros2
Zeros (%)0.1%
Negative3
Negative (%)0.2%
Memory size10.7 KiB
2024-04-13T09:57:05.326952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.52287875
5-th percentile0.69897
Q10.90308999
median1.0863598
Q31.1139434
95-th percentile1.3617278
Maximum2.0334238
Range2.5563025
Interquartile range (IQR)0.21085337

Descriptive statistics

Standard deviation0.26769371
Coefficient of variation (CV)0.26740216
Kurtosis3.393093
Mean1.0010903
Median Absolute Deviation (MAD)0.11776015
Skewness-0.51656162
Sum1360.4817
Variance0.071659922
MonotonicityNot monotonic
2024-04-13T09:57:05.540108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1.113943352 442
32.5%
0.903089987 291
21.4%
0.6989700043 218
16.0%
1.204119983 126
 
9.3%
1.079181246 102
 
7.5%
0.3010299957 41
 
3.0%
1.681241237 39
 
2.9%
0.5051499783 11
 
0.8%
1.361727836 11
 
0.8%
1.322219295 10
 
0.7%
Other values (22) 68
 
5.0%
ValueCountFrequency (%)
-0.5228787453 3
 
0.2%
0 2
 
0.1%
0.3010299957 41
 
3.0%
0.4771212547 3
 
0.2%
0.5051499783 11
 
0.8%
0.6989700043 218
16.0%
0.903089987 291
21.4%
0.9395192526 1
 
0.1%
1 4
 
0.3%
1.079181246 102
 
7.5%
ValueCountFrequency (%)
2.033423755 1
 
0.1%
1.806179974 6
 
0.4%
1.681241237 39
2.9%
1.612783857 1
 
0.1%
1.602059991 4
 
0.3%
1.505149978 2
 
0.1%
1.397940009 1
 
0.1%
1.380211242 4
 
0.3%
1.361727836 11
 
0.8%
1.33243846 2
 
0.1%

Front camera
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67212026
Minimum-0.52287875
Maximum1.6812412
Zeros18
Zeros (%)1.3%
Negative87
Negative (%)6.4%
Memory size10.7 KiB
2024-04-13T09:57:05.745772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.52287875
5-th percentile-0.52287875
Q10.30103
median0.69897
Q30.90308999
95-th percentile1.30103
Maximum1.6812412
Range2.20412
Interquartile range (IQR)0.60205999

Descriptive statistics

Standard deviation0.44463172
Coefficient of variation (CV)0.66153595
Kurtosis1.1879946
Mean0.67212026
Median Absolute Deviation (MAD)0.20411998
Skewness-0.95091861
Sum913.41144
Variance0.19769736
MonotonicityNot monotonic
2024-04-13T09:57:05.910501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.6989700043 464
34.1%
0.903089987 270
19.9%
0.3010299957 205
15.1%
1.204119983 97
 
7.1%
-0.5228787453 86
 
6.3%
1.113943352 55
 
4.0%
1.301029996 25
 
1.8%
1.505149978 23
 
1.7%
0 18
 
1.3%
0.1139433523 17
 
1.3%
Other values (20) 99
 
7.3%
ValueCountFrequency (%)
-0.5228787453 86
6.3%
-0.04575749056 1
 
0.1%
0 18
 
1.3%
0.04139268516 2
 
0.1%
0.07918124605 6
 
0.4%
0.1139433523 17
 
1.3%
0.1760912591 1
 
0.1%
0.2041199827 3
 
0.2%
0.278753601 4
 
0.3%
0.3010299957 205
15.1%
ValueCountFrequency (%)
1.681241237 1
 
0.1%
1.602059991 1
 
0.1%
1.505149978 23
 
1.7%
1.397940009 13
 
1.0%
1.380211242 16
 
1.2%
1.301029996 25
 
1.8%
1.204119983 97
7.1%
1.113943352 55
4.0%
1.079181246 8
 
0.6%
1 6
 
0.4%

Operating system
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17807211
Minimum0
Maximum6
Zeros1299
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-13T09:57:06.041087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92059825
Coefficient of variation (CV)5.1698059
Kurtosis28.526607
Mean0.17807211
Median Absolute Deviation (MAD)0
Skewness5.4193687
Sum242
Variance0.84750114
MonotonicityNot monotonic
2024-04-13T09:57:06.158691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1299
95.6%
5 19
 
1.4%
6 17
 
1.3%
2 10
 
0.7%
1 10
 
0.7%
4 3
 
0.2%
3 1
 
0.1%
ValueCountFrequency (%)
0 1299
95.6%
1 10
 
0.7%
2 10
 
0.7%
3 1
 
0.1%
4 3
 
0.2%
5 19
 
1.4%
6 17
 
1.3%
ValueCountFrequency (%)
6 17
 
1.3%
5 19
 
1.4%
4 3
 
0.2%
3 1
 
0.1%
2 10
 
0.7%
1 10
 
0.7%
0 1299
95.6%

Wi-Fi
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
1
1351 
0
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1359
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1351
99.4%
0 8
 
0.6%

Length

2024-04-13T09:57:06.318952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T09:57:06.498206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1351
99.4%
0 8
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 1351
99.4%
0 8
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1359
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1351
99.4%
0 8
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1351
99.4%
0 8
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1351
99.4%
0 8
 
0.6%

Bluetooth
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
1
1344 
0
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1359
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1344
98.9%
0 15
 
1.1%

Length

2024-04-13T09:57:06.625713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T09:57:06.782344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1344
98.9%
0 15
 
1.1%

Most occurring characters

ValueCountFrequency (%)
1 1344
98.9%
0 15
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1359
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1344
98.9%
0 15
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1344
98.9%
0 15
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1344
98.9%
0 15
 
1.1%

GPS
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
1
1251 
0
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1359
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1251
92.1%
0 108
 
7.9%

Length

2024-04-13T09:57:06.904417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T09:57:07.058627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1251
92.1%
0 108
 
7.9%

Most occurring characters

ValueCountFrequency (%)
1 1251
92.1%
0 108
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1359
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1251
92.1%
0 108
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1251
92.1%
0 108
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1251
92.1%
0 108
 
7.9%

Number of SIMs
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
0.3010299956639812
1131 
0.0
227 
0.47712125471966244
 
1

Length

Max length19
Median length18
Mean length15.495217
Min length3

Characters and Unicode

Total characters21058
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.3010299956639812
2nd row0.3010299956639812
3rd row0.3010299956639812
4th row0.3010299956639812
5th row0.0

Common Values

ValueCountFrequency (%)
0.3010299956639812 1131
83.2%
0.0 227
 
16.7%
0.47712125471966244 1
 
0.1%

Length

2024-04-13T09:57:07.194702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T09:57:07.349294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.3010299956639812 1131
83.2%
0.0 227
 
16.7%
0.47712125471966244 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
9 4525
21.5%
0 3848
18.3%
1 2265
10.8%
2 2265
10.8%
6 2264
10.8%
3 2262
10.7%
. 1359
 
6.5%
5 1132
 
5.4%
8 1131
 
5.4%
4 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19699
93.5%
Other Punctuation 1359
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 4525
23.0%
0 3848
19.5%
1 2265
11.5%
2 2265
11.5%
6 2264
11.5%
3 2262
11.5%
5 1132
 
5.7%
8 1131
 
5.7%
4 4
 
< 0.1%
7 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1359
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21058
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 4525
21.5%
0 3848
18.3%
1 2265
10.8%
2 2265
10.8%
6 2264
10.8%
3 2262
10.7%
. 1359
 
6.5%
5 1132
 
5.4%
8 1131
 
5.4%
4 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 4525
21.5%
0 3848
18.3%
1 2265
10.8%
2 2265
10.8%
6 2264
10.8%
3 2262
10.7%
. 1359
 
6.5%
5 1132
 
5.4%
8 1131
 
5.4%
4 4
 
< 0.1%

3G
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
1
1214 
0
145 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1359
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1214
89.3%
0 145
 
10.7%

Length

2024-04-13T09:57:07.495542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T09:57:07.656168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1214
89.3%
0 145
 
10.7%

Most occurring characters

ValueCountFrequency (%)
1 1214
89.3%
0 145
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1359
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1214
89.3%
0 145
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1214
89.3%
0 145
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1214
89.3%
0 145
 
10.7%

4G/ LTE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
1
1012 
0
347 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1359
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1012
74.5%
0 347
 
25.5%

Length

2024-04-13T09:57:07.800207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T09:57:07.973325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1012
74.5%
0 347
 
25.5%

Most occurring characters

ValueCountFrequency (%)
1 1012
74.5%
0 347
 
25.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1359
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1012
74.5%
0 347
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1012
74.5%
0 347
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1012
74.5%
0 347
 
25.5%

Price
Real number (ℝ)

HIGH CORRELATION 

Distinct627
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11465.826
Minimum494
Maximum174990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-13T09:57:08.149752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum494
5-th percentile2990
Q14763.5
median6999
Q311999
95-th percentile35990
Maximum174990
Range174496
Interquartile range (IQR)7235.5

Descriptive statistics

Standard deviation13857.497
Coefficient of variation (CV)1.2085913
Kurtosis33.347917
Mean11465.826
Median Absolute Deviation (MAD)2978
Skewness4.6074619
Sum15582057
Variance1.9203023 × 108
MonotonicityNot monotonic
2024-04-13T09:57:08.358725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4999 38
 
2.8%
6999 32
 
2.4%
3999 28
 
2.1%
8999 28
 
2.1%
7999 25
 
1.8%
5999 25
 
1.8%
9999 24
 
1.8%
2999 22
 
1.6%
3499 15
 
1.1%
6499 14
 
1.0%
Other values (617) 1108
81.5%
ValueCountFrequency (%)
494 1
 
0.1%
994 2
 
0.1%
1249 1
 
0.1%
1999 9
0.7%
2000 1
 
0.1%
2190 1
 
0.1%
2199 4
0.3%
2200 1
 
0.1%
2222 1
 
0.1%
2240 1
 
0.1%
ValueCountFrequency (%)
174990 1
0.1%
164999 1
0.1%
106900 1
0.1%
96900 1
0.1%
92999 1
0.1%
88719 1
0.1%
83900 1
0.1%
79699 1
0.1%
77299 1
0.1%
74990 1
0.1%

logTranforedPrice
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.905298
Minimum2.69
Maximum5.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-13T09:57:08.581769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.69
5-th percentile3.48
Q13.68
median3.85
Q34.08
95-th percentile4.56
Maximum5.24
Range2.55
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.33093928
Coefficient of variation (CV)0.084741107
Kurtosis0.82660746
Mean3.905298
Median Absolute Deviation (MAD)0.19
Skewness0.78916561
Sum5307.3
Variance0.10952081
MonotonicityNot monotonic
2024-04-13T09:57:08.784892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7 60
 
4.4%
3.95 53
 
3.9%
3.9 48
 
3.5%
3.85 46
 
3.4%
3.6 45
 
3.3%
4 42
 
3.1%
3.78 42
 
3.1%
3.65 30
 
2.2%
3.81 29
 
2.1%
3.74 26
 
1.9%
Other values (144) 938
69.0%
ValueCountFrequency (%)
2.69 1
 
0.1%
3 2
 
0.1%
3.1 1
 
0.1%
3.3 10
0.7%
3.34 6
0.4%
3.35 2
 
0.1%
3.36 2
 
0.1%
3.37 2
 
0.1%
3.38 4
 
0.3%
3.39 1
 
0.1%
ValueCountFrequency (%)
5.24 1
0.1%
5.22 1
0.1%
5.03 1
0.1%
4.99 1
0.1%
4.97 1
0.1%
4.95 1
0.1%
4.92 1
0.1%
4.9 1
0.1%
4.89 1
0.1%
4.88 1
0.1%

latest_tech_stack
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
1
894 
0
465 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1359
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 894
65.8%
0 465
34.2%

Length

2024-04-13T09:57:08.989061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T09:57:09.117218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 894
65.8%
0 465
34.2%

Most occurring characters

ValueCountFrequency (%)
1 894
65.8%
0 465
34.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1359
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 894
65.8%
0 465
34.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 894
65.8%
0 465
34.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 894
65.8%
0 465
34.2%

Interactions

2024-04-13T09:56:55.772685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:25.611632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:28.186390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:30.882621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:33.363662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:35.704209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:38.175137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:41.178596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:43.557977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:46.280222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:48.591654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:50.838666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:53.233733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:55.945761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:25.800494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:28.382188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:31.070267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:33.564780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:35.894231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:38.374795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:41.367122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:43.753130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:46.467767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:48.769237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:51.023800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:53.420455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:56.154944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:26.019421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:28.648999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:31.274394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:33.742893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:36.136927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:38.552390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:41.556155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:43.970791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:46.657319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:48.954913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:51.208023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:53.666482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:56.316042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:26.208119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:28.866112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:31.440018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:33.909040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:36.320119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:38.739629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:41.737248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:44.196396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:46.846328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:49.123263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:51.430159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:53.832673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:56.481632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:26.403668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:29.058663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:31.609712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:34.073288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:36.486273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:38.929003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:41.915295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:44.409564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:47.046321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:49.295327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:51.623225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:54.009994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:56.701791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:26.569570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:29.331518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:31.776767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:34.242833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:36.656904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:39.112072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:42.084163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:44.594674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:47.221414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:49.470870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:51.798280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:54.200775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:56.907845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:26.774686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:29.510843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:31.989819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:34.409333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:36.828059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:39.332129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:42.249797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:44.810723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:47.397512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:49.632038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:51.984237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:54.402418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:57.107885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:26.967499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:29.675990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:32.199219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:34.569585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:36.998174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:39.535798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:42.436510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:45.049789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:47.570824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:49.784363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:52.172367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:54.576962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:57.330688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:27.176638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:29.873534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:32.426913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:34.744150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:37.186432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:39.767871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:42.622580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:45.258382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:47.749571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:49.959953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:52.373492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:54.793495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:57.521227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:27.398762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:30.090845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:32.605983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:34.922280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:37.374505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:39.938360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:42.787729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:45.481027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:47.908851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:50.127934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:52.552565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:54.985853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:57.700264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:27.598367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:30.274665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:32.767959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:35.095766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:37.565106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:40.153967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:42.991199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:45.652607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:48.084932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:50.288666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:52.714778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:55.198907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:57.886379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:27.808193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:30.493787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:33.016242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:35.264301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:37.741201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:40.385335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:43.175261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:45.838496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:48.258456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:50.471273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:52.897853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:55.410537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:58.082446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:28.010405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:30.694915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:33.199396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:35.497560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:37.906455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:40.985894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:43.367819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:46.058630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:48.429548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:50.665798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:53.068525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-04-13T09:56:55.606351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2024-04-13T09:57:09.263845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ModelBattery capacity (mAh)Screen size (inches)Resolution xResolution yProcessorRAM (MB)Internal storage (GB)Rear cameraFront cameraOperating systemPricelogTranforedPriceTouchscreenWi-FiBluetoothGPSNumber of SIMs3G4G/ LTElatest_tech_stack
Model1.0000.1400.1160.2030.1870.0940.1940.1940.1950.1410.1630.2430.2430.0000.0000.1120.0000.1360.0600.2220.209
Battery capacity (mAh)0.1401.0000.7930.5860.6830.5690.7090.7030.6310.656-0.1540.5190.5190.1750.0000.0000.0980.0000.1030.3890.364
Screen size (inches)0.1160.7931.0000.6420.7730.6480.7630.7680.6880.722-0.1260.5920.5930.3720.0000.0000.1300.0540.1200.3130.297
Resolution x0.2030.5860.6421.0000.9070.6150.7650.7450.7220.620-0.0030.7060.7070.2590.0000.0000.1170.1390.0970.3780.371
Resolution y0.1870.6830.7730.9071.0000.6870.8290.8240.7660.711-0.0510.7200.7210.3120.0000.0000.1800.1410.1680.3900.385
Processor0.0940.5690.6480.6150.6871.0000.6910.6770.6300.648-0.2020.5230.5230.1010.0820.0470.0410.1540.0550.2750.254
RAM (MB)0.1940.7090.7630.7650.8290.6911.0000.9290.7800.797-0.0920.7030.7040.2150.0000.0000.1670.0000.1050.4800.435
Internal storage (GB)0.1940.7030.7680.7450.8240.6770.9291.0000.7570.786-0.0500.6980.6990.1770.0000.0000.1490.0000.1300.4580.409
Rear camera0.1950.6310.6880.7220.7660.6300.7800.7571.0000.702-0.0980.6500.6510.2300.0000.0370.1140.0540.0980.3990.370
Front camera0.1410.6560.7220.6200.7110.6480.7970.7860.7021.000-0.1550.5330.5340.2060.0000.0000.0880.1060.0990.4490.395
Operating system0.163-0.154-0.126-0.003-0.051-0.202-0.092-0.050-0.098-0.1551.0000.1030.1030.1470.0000.0000.0000.2220.0450.0560.067
Price0.2430.5190.5920.7060.7200.5230.7030.6980.6500.5330.1031.0001.0000.0000.0000.0000.0130.1440.0360.1110.153
logTranforedPrice0.2430.5190.5930.7070.7210.5230.7040.6990.6510.5340.1031.0001.0000.1420.0000.0000.1280.1690.0000.2910.309
Touchscreen0.0000.1750.3720.2590.3120.1010.2150.1770.2300.2060.1470.0000.1421.0000.0210.0000.0070.0410.0510.0570.147
Wi-Fi0.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0211.0000.2200.0980.0000.1100.0170.093
Bluetooth0.1120.0000.0000.0000.0000.0470.0000.0000.0370.0000.0000.0000.0000.0000.2201.0000.0540.0000.0600.0000.136
GPS0.0000.0980.1300.1170.1800.0410.1670.1490.1140.0880.0000.0130.1280.0070.0980.0541.0000.0990.1740.0760.404
Number of SIMs0.1360.0000.0540.1390.1410.1540.0000.0000.0540.1060.2220.1440.1690.0410.0000.0000.0991.0000.2390.1120.089
3G0.0600.1030.1200.0970.1680.0550.1050.1300.0980.0990.0450.0360.0000.0510.1100.0600.1740.2391.0000.3520.476
4G/ LTE0.2220.3890.3130.3780.3900.2750.4800.4580.3990.4490.0560.1110.2910.0570.0170.0000.0760.1120.3521.0000.810
latest_tech_stack0.2090.3640.2970.3710.3850.2540.4350.4090.3700.3950.0670.1530.3090.1470.0930.1360.4040.0890.4760.8101.000

Missing values

2024-04-13T09:56:58.458122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T09:56:59.037766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NameBrandModelBattery capacity (mAh)Screen size (inches)TouchscreenResolution xResolution yProcessorRAM (MB)Internal storage (GB)Rear cameraFront cameraOperating systemWi-FiBluetoothGPSNumber of SIMs3G4G/ LTEPricelogTranforedPricelatest_tech_stack
0OnePlus 7T Pro McLaren EditionOnePlus493.6111920.82412613.1583623.4941550.9030904.0791812.408241.6812411.20412001110.3010311589984.771
1Realme X2 ProRealme11423.6020600.81291313.0334243.3802110.9030903.7781511.806181.8061801.20412001110.3010311279994.451
2iPhone 11 Pro MaxApple12883.5986810.81291313.0941223.4294290.7781513.6020601.806181.0791811.07918161110.30103111069005.031
3iPhone 11Apple12863.4927600.78533012.9180303.2533380.7781513.6020601.806181.0791811.07918161110.3010311629004.801
4LG G8X ThinQLG5223.6020600.80618013.0334243.3692160.9030903.7781512.107211.0791811.50515001110.0000000499904.700
5OnePlus 7TOnePlus473.5797840.81624113.0334243.3802110.9030903.9030902.107211.6812411.20412001100.3010311349304.540
6OnePlus 7T ProOnePlus483.6111920.82412613.1583623.4941550.9030903.9030902.408241.6812411.20412001110.3010311529904.721
7Samsung Galaxy Note 10+Samsung5843.6334680.83250913.1583623.4828740.9030904.0791812.408241.0791811.00000001110.3010311796994.901
8Asus ROG Phone 2Asus9363.7781510.81888513.0334243.3692160.9030903.9030902.107211.6812411.38021101110.0000011379994.581
9Xiaomi Redmi K20 ProXiaomi9593.6020600.80550113.0334243.3692160.9030903.7781512.107211.6812411.30103001110.3010300231904.370
NameBrandModelBattery capacity (mAh)Screen size (inches)TouchscreenResolution xResolution yProcessorRAM (MB)Internal storage (GB)Rear cameraFront cameraOperating systemWi-FiBluetoothGPSNumber of SIMs3G4G/ LTEPricelogTranforedPricelatest_tech_stack
1349Micromax Bolt D303Micromax2663.1139430.60206012.6812412.9030900.301032.709270.602060.505150-0.52287901110.301031029843.470
1350Vivo V1Vivo10563.3617280.69897012.8573323.1072100.602063.301031.204121.1139430.69897001110.3010311153004.181
1351Panasonic Eluga ZPanasonic4573.3117540.69897012.8573323.1072100.903093.301031.204121.1139430.69897001110.301031055293.740
1352Xolo EraXolo4663.3222190.69897012.9314582.6812410.602063.000000.903090.9030900.30103001110.301031036993.570
1353Intex Aqua Y2 UltraIntex2233.1461280.60206012.6812412.9030900.602063.000000.903090.698970-0.52287901110.301031035993.560
1354Intex Aqua A2Intex1483.1760910.60206012.6812412.9030900.602062.709270.903090.698970-0.52287901110.301031025993.410
1355Videocon Infinium Z51 Nova+Videocon6523.3010300.69897012.6812412.9314580.602063.000000.903090.9030900.69897001110.301031029403.470
1356Intex Aqua Y4Intex2243.2304490.65321312.6812412.9314580.301032.709270.602060.6989700.30103001100.301031029993.480
1357iBall Andi4 B20iBall1403.0969100.60206012.6812412.9030900.000002.40824-0.290730.301030-0.52287901110.301031024983.400
1358iBall Andi Avonte 5iBall1373.3324380.69897012.6812412.9314580.602063.000000.903090.9030900.00000001110.301031039993.600